The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.
Sizes of objects in an image are often used to enhance accuracy of an image analysis algorithm of an image monitoring system. For example, when only humans are set as objects-of-interest, approximate sizes of humans are preset in the system such that nonhuman objects (animals, vehicles, etc.) are excluded from target objects to detect.
The inventor(s) has noted that to estimate the size of an object, precise camera calibration is needed. The inventor(s) has noted that such precise camera calibration requires complex processing and is thus not proper for practical use.
The inventor(s) has noted that the size of an object in an image on the image monitoring system varies depending on the coordinates thereof in the image due to perspective effect. The inventor(s) has noted that known technology involves a user in person to input the size of an object-of-interest at a few different points in an image, perform interpolation on the object size information inputted in order to estimate and use the size of the object at each coordinate in the image.
The inventor(s) has noted that the result obtained through this method varies according to the sample that the user inputs, and therefore the accuracy of the system depends on the competence level of the user. The inventor(s) has experienced that to apply the method requiring inputs from the user to a large-scale system which employs multiple cameras, a lot of labor is necessary.
The inventor(s) has noted that the image monitoring technology only counts on the size of an object. The inventor(s) has also noted that the pose of an object plays an important role in improving the accuracy of an algorithm. For example, in a region-based algorithm for estimating the number of people, the area that a person occupies in an image plays a very important role as a scaling factor. The inventor(s) has experienced that when the posture of the person is slanted, the size of the minimum bounding rectangle or box of the corresponding object greatly differs from the actual area of the object, which will degrade the accuracy of the algorithm. Further, the inventor(s) has experienced that when a recognition-based technique is used, the recognition performance is degraded unless an object is input making the same pose as the image used for a classifier in the learning process for recognition.